Abstract

Pulmonary fissures are important landmarks for automated recognition of lung anatomy. We propose a derivative of stick (DoS) filter for fissure detection in CT scans by considering the thin linear shape across multiple transverse planes. Based on a stick decomposition of a rectangle neighborhood, our main contribution is to define a nonlinear derivative vertical to the stick orientation. Then, combining with a standard deviation of intensity along the stick, the composed likelihood function will take a strong response to fissure-like bright lines, and tends to suppress undesired structures including large vessels, step edges and blobs. Applying the 2D filter sequentially to the sagittal, coronal and axial planes, an approximate 3D co-planar constraint is implicitly exerted through the cascaded pipeline, which helps to further remove the non-fissure tissues. To generate a clear segmentation, we adopt a connected component based post-processing scheme, and a branch-point finding algorithm is introduced to disconnect the residual adjacent clutters from the fissures, after binarizing the filter response with a relatively low threshold. The performance of our filter has been verified in experiments with a 23 patients dataset, where pathological deformations to different extents are included. It compared favorably with prior algorithms.